Extreme heat and pediatric health in a warming world: a space-time stratified case-crossover investigation in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Globally, climate change is causing frequent and severe extreme heat events (EHEs). A large body of literature links EHEs to multiple health endpoints. While children's physiology and activity patterns differ from those of adults in ways that are hypothesized to increase susceptibility to such endpoints, research gaps remain regarding the specific impacts of EHEs on child health. This study evaluated pediatric emergency healthcare utilizations associated with EHEs in Ontario. METHODS: Applying a space-time stratified case-crossover design, associations between EHEs (same-day or lagged exposure to 2 consecutive days of daily maximum temperatures above percentile thresholds) and 15 causes of pediatric emergency healthcare use in Ontario, Canada from 2005 to 2015 were analysed using conditional quasi-Poisson regression. In primary analyses, EHEs were defined as two or more consecutive days with temperatures above the 99th percentile of temperature within each respective forward sortation area (FSA). Emergency healthcare use was measured using hospital admissions as an indicator of severe outcomes, and emergency department (ED) visits as a sensitive measure of outcomes. RESULTS: Relative to non-EHE days, EHEs increased the rates of pediatric hospital admissions for respiratory illnesses by 26% (95% CI: 14-40%), asthma by 29% (16-44%); infectious and parasitic diseases by 36% (24-50%), lower respiratory infections by 50% (36-67%), and enteritis by 19% (7-32%). EHEs also increased the rates of ED visits for lower respiratory infections by 10% (0-21%), asthma by 18% (7-29%), heat-related illnesses by 211% (193-230%), heatstroke by 590% (550-622%), and dehydration by 35% (25-46%), but not for other causes. Admissions and ED visits due to injuries and transportation related injuries were negatively associated with EHEs. Neither all-cause hospital admissions nor ED visits were associated with EHEs. CONCLUSIONS: In Ontario, EHEs decreased the rates of pediatric emergency healthcare utilization for injuries and increased the rates of respiratory illnesses, asthma, heat-related illnesses, heatstroke, dehydration, infectious and parasitic diseases, lower respiratory infections, and enteritis. Tailored policies and programs that reflect the specific heat-related vulnerabilities of children to respiratory and infectious illnesses are warranted in the face of a rapidly warming climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".